JOURNAL ARTICLE

AIENP‐Reinforced DISCO Method for Whole‐Tissue 3D Reconstruction of Pulmonary Capillaries.

  • Published In: Advanced Functional Materials, 2024, v. 34, n. 13. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gong, Xiao‐Ting; Chong, Kok Chan; Liu, Jiaqi; Cheng, Wei; Yang, Jing; Liu, Bin 3 of 3

Abstract

The pulmonary vascular system plays a crucial role in maintaining normal physiological functions, and perturbations in this network often serve as indicators for various fatal diseases. Thus, accurate mapping and assessment of the intricate anatomical details of pulmonary vasculature is essential for the investigation of the underlying mechanism of these diseases. Yet it is considered a tough challenge as traditional imaging techniques offer limited representations of the vasculature network in the lung, while optical imaging methods face limitations from tissue depth. To overcome these obstacles, in this study, an AIENP‐reinforced DISCO method, for whole‐tissue 3D reconstruction of pulmonary capillaries is presented. Combining AIENPs, hydrogel‐enhanced scaffolds, and solvent‐based DISCO procedures, the method successfully visualizes the entire network of mouse pulmonary capillaries with a significantly shortened timeframe and cost. The whole process including labeling and clearing takes 6 days and it costs ≈ 5 USD to stain the lung vasculature of an adult mouse. Moreover, the study provides valuable insights for detecting pulmonary vascular abnormalities. This fast and cost‐effective technique opens new avenues for developing better fluorophores compatible with tissue optical clearing and offers insights for in‐depth research on pulmonary pathophysiology. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Advanced Functional Materials. 2024/03, Vol. 34, Issue 13, p1
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:1616-301X
  • DOI:10.1002/adfm.202312176
  • Accession Number:176294593
  • Copyright Statement:Copyright of Advanced Functional Materials is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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